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On-road emissions, a dominant source of urban air pollution, damage human health. Emissions increase air pollution intake (and damage health) of travelers (internal costs), and of non-travelers (external costs). This research constructs a framework modeling the microscopic production of emission cost from the vehicle and link level and applies it to a metropolitan road network. It uses project-level Motor Vehicle Emission Simulator (MOVES) simulations to model link-specific on-road emissions, and then employs the RLINE dispersion model to estimate on- and off-road concentrations of pollutants from vehicles. The internal and external emission costs are measured accordingly by counting the health damage costs of travelers and general population because of exposure. The framework is applied to the Minneapolis-St. Paul (Twin Cities) Metropolitan Area as a proof-of-concept. The estimates show that highways have higher emission concentrations because of higher traffic flow, but that the internal and external emission costs per vehicle kilometer traveled are lower. The emission costs that commuters impose on others greatly exceeds that which they bear. This modeling process is replicable for planners and practitioners assessing emission costs in other regions.

It is commonly seen that accessibility is measured considering only one opportunity or activity type or purpose of interest, e.g., jobs. The value of a location, and thus the overall access, however, depends on the ability to reach many different types of opportunities. This paper clarifies the concept of multi-activity accessibility, which combines multiple types of opportunities into a single aggregated access measure, and aims to find more comprehensive answers for the questions: what is being accessed, by what extent, and how it varies by employment status and by gender. The Minneapolis - St. Paul metropolitan region is selected for the measurement of multi-activity accessibility, using both primal and dual measures of cumulative access, for auto and transit. It is hypothesized that workers and non-workers, and males and females have different accessibility profiles. This research demonstrates its practicality at the scale of a metropolitan area, and highlights the differences in access for workers and non-workers, and men and women, because of differences in their activity participation.

This study measures accessibility by automobile for the Minneapolis - Saint Paul (Twin Cities) region from 1995 to 2005. In contrast to most previous analyses of accessibility, this study uses travel time estimates derived, to the extent possible, from actual observations of network performance by time of day. A set of cumulative opportunity measures are computed with transport analysis zones (TAZs) as the unit of analysis for 1995 and 2005. Analysis of the changes in accessibility by location over the period of study reveals that, for the majority of locations in the region, accessibility increased over this period, though the increases were not uniform. A “flattening” or convergence of levels of accessibility across locations was observed over time, with faster-growing suburban locations gaining the most in terms of employment accessibility. An effort to decompose the causes of changes in accessibility into components related to transport network structure and land use (opportunity location) reveals that both causes make a contribution to increasing accessibility, though the effects of changes to the transportation network tend to be more location-specific. Overall, the results of the study demonstrate the feasibility and relevance of using accessibility as a key performance measure to describe the regional transport system.

Traditionally accessibility has been analyzed from the perspective of the mean or expected travel time, which fails to capture the full cost, especially the external cost, of travel. The full cost accessibility (FCA) framework, proposed by Cui and Levinson (2018), provides a theoretical basis to fill the gap. It combines temporal, monetary, and non-monetary internal and external travel costs into accessibility evaluations, considering the time cost, crash cost, emission cost, and monetary cost. This paper extends the FCA framework and measures the full cost accessibility by auto for the Minneapolis - St. Paul Metropolitan area, demonstrating the practicality of the FCA framework on real networks.

Traditional accessibility evaluation fails to fully capture the travel costs, especially the external costs, of travel. This study develops a full cost accessibility (FCA) framework by combining the internal and external cost components of travel time, safety, emissions, and money. The example illustrated compares FCA by automobile and bicycle on a toy network to demonstrate the potential and practicality of applying the FCA framework on real networks. This method provides an efficient evaluation tool for transport planning projects.

This study measures the variability of job accessibility via automobile for the Minneapolis-St. Paul region. The accessibility analysis uses cumulative opportunity measures. The travel times on the network are tested at various level (10th percentile speed, 50th percentile speed, 90th percentile speed) using the TomTom speed data for 2010. It is shown that accessibility varies widely day-to-day as travel speeds on the network vary. Some parts of the region (a ring around the core) have more volatility in accessibility (and are thus less reliable) than others.

Risk severity in transportation network analysis is defined as the effects of a link or network failure on the whole system. Change accessibility (reduction in the number of jobs which can be reached) is used as an integrated indicator to reflect the severity of a link outage. The changes of accessibility before-and-after the removing of a freeway segment from the network represent its risk severity. The analysis in the Minneapolis - St. Paul (Twin Cities) region show that links near downtown Minneapolis have relative higher risk severity than those in rural area. The geographical distribution of links with the highest risk severity displays the property that these links tend to be near or at the intersection of freeways. Risk severity of these links based on the accessibility to jobs and to workers at different time thresholds and during different dayparts are also analyzed in the paper. The research finds that network structure measures: betweenness, straightness and closeness, help explain the severity of loss due to network outage.

On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods.